Final Project: A protein expression analysis of Breast Cancer
Introduction
The data set consists of:
iTRAQ proteome profiling of 77 breast cancer samples + 3 healthy samples, with expression values for ~12.000 proteins for each sample.
A file containing the clinical data of the 77 breast cancer patients (TCGA ID, sex, age, tumor receptors, etc.).
A file containing the list of genes and proteins used by the PAM50 classification system.
The analysis of this data set is relevant for multiple potential applications: expression analysis to identify biomarkers, understand disease heterogeneity, and infer personalized treatment strategies in breast cancer.
Materials and methods
Workflow
Materials and methods
Description of relevant variables
Dropped variables (redundant or not relevant): Survival.Data.Form, Days.to.date.of.Death, Days.to.Date.of.Last.Contact, OS.Time, Vital.Status, Tumor..T1.Coded, Metastasis.Coded, AJCC.Stage, Converted.Stage and all the columns destined to cluster the data
Created variables:
Age.Ini.Diagnostic.group: intervals of 10 years starting from 30 and going up until 90.
Age.Menopausal.group:
[30, 45)Pre-menopausal,[45, 55)Menopausal,[55, 90)Post-menopausal.ER_PR_HER2: level from 0 to 7 depending on hormonal receptors (ER, PR) present an the level of HER2.
TNBC: 0 if positive, 1 if negative
AJCC.Simp: simplified AJCC stages (I, II, III, and IV)
Materials and methods
Description of relevant variables
Analysis 1. PCA.
Analysis 3: Differential expression
Two functions were created allowing to easily perform several comparisons with the present data.
DEA_proteins()
DEA_proteins <- function(data_in, condition_test){
col_name <- deparse(substitute(condition_test))
data_long <- data_in |>
dplyr::select(matches("^NP"),
matches("^XP"),
matches("^YP"),
{{ condition_test }}) |>
pivot_longer(cols = -{{ condition_test }},
names_to = "Protein",
values_to = "log2_iTRAQ")
data_long_nested <- data_long |>
group_by(Protein) |>
nest() |>
ungroup()
data_w_model <- data_long_nested |>
group_by(Protein) |>
mutate(model_object = map(.x = data,
.f = ~lm(formula = str_c("log2_iTRAQ ~", col_name) ,
data = .x)))
data_w_model <- data_w_model |>
mutate(model_object_tidy = map(.x = model_object,
.f = ~tidy(.x,
conf.int = TRUE,
conf.level = 0.95)))
estimates <- data_w_model |>
unnest(model_object_tidy) |>
filter(term == col_name) |>
ungroup() |>
dplyr::select(Protein, p.value, estimate, conf.low, conf.high) |>
mutate(q.value = p.adjust(p.value)) |>
mutate(dif_exp = case_when(q.value <= 0.05 & estimate > 0 ~ "Up",
q.value <= 0.05 & estimate < 0 ~ "Down",
q.value > 0.05 ~ 'NS'))
plt_volcano <- volcano_plot(estimates, col_name)
return(list(estimates=estimates, plt_volcano=plt_volcano))
}volcano_plot()
volcano_plot <- function(data, condition_test){
plt <- data |>
group_by(dif_exp) |>
mutate(label = case_when(dif_exp == "Up" ~ str_c(dif_exp,
" (Count: ",
n(),
")" ),
dif_exp == "Down" ~ str_c(dif_exp,
" (Count: ",
n(),
")" ),
dif_exp == "NS" ~ str_c(dif_exp))) |>
ggplot(aes(x = estimate,
y = -log10(p.value),
colour = label)) +
geom_point(alpha = 0.4,
shape = "circle") +
labs(title = str_c("Differentially expressed proteins in the test: ",
condition_test,
" vs. Non-",
condition_test),
subtitle = "Proteins highlighted in either red or blue were
\nsignificant after multiple test correction",
x = "Estimates",
y = expression(-log[10]~(p)),
color = "Differential expression") +
scale_color_manual(values = c("blue",
"grey",
"red")) +
theme_minimal() +
theme(legend.position = "right",
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
return(plt)
}Analysis 3: Differential expression
NP_002094.2: glycogen [starch] synthase, muscle isoform 1